{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CSV/Excel Analysis Agent\n",
    "\n",
    "- Author: [Hye-yoon Jeong](https://github.com/Hye-yoonJeong)\n",
    "- Peer Review: \n",
    "- Proofread : [BokyungisaGod](https://github.com/BokyungisaGod)\n",
    "- This is a part of [LangChain Open Tutorial](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial)\n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LangChain-OpenTutorial/LangChain-OpenTutorial/blob/main/15-Agent/07-CSV-Excel-Agent.ipynb)[![Open in GitHub](https://img.shields.io/badge/Open%20in%20GitHub-181717?style=flat-square&logo=github&logoColor=white)](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial/blob/main/15-Agent/07-CSV-Excel-Agent.ipynb)\n",
    "## Overview\n",
    "\n",
    "This tutorial covers how to create an agent that performs analysis on the ```Pandas``` DataFrame loaded from CSV or Excel files. The agent generates ```Pandas``` queries to analyze the dataset.\n",
    "\n",
    "### Table of Contents\n",
    "\n",
    "- [Overview](#overview)\n",
    "- [Environment Setup](#environment-setup)\n",
    "- [Sample Data](#sample-data)\n",
    "- [Create an Analysis Agent](#create-an-analysis-agent)\n",
    "\n",
    "### References\n",
    "- [LangChain Documentation : create_pandas_dataframe_agent](https://python.langchain.com/api_reference/experimental/agents/langchain_experimental.agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent.html)\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment Setup\n",
    "\n",
    "Set up the environment. You may refer to [Environment Setup](https://wikidocs.net/257836) for more details.\n",
    "\n",
    "**[Note]**\n",
    "- ```langchain-opentutorial``` is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials. \n",
    "- You can check out the [```langchain-opentutorial``` ](https://github.com/LangChain-OpenTutorial/langchain-opentutorial-pypi) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 24.1 -> 24.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "%%capture --no-stderr\n",
    "%pip install langchain-opentutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install required packages\n",
    "from langchain_opentutorial import package\n",
    "\n",
    "package.install(\n",
    "    [\n",
    "        \"langsmith\",\n",
    "        \"langchain\",\n",
    "        \"langchain_core\",\n",
    "        \"langchain_openai\",\n",
    "        \"langchain_community\",\n",
    "        \"langchain_experimental\",\n",
    "    ],\n",
    "    verbose=False,\n",
    "    upgrade=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv(override=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment variables have been set successfully.\n"
     ]
    }
   ],
   "source": [
    "# Set environment variables\n",
    "from langchain_opentutorial import set_env\n",
    "\n",
    "set_env(\n",
    "    {\n",
    "        # \"OPENAI_API_KEY\": \"\",\n",
    "        # \"LANGCHAIN_API_KEY\": \"\",\n",
    "        \"LANGCHAIN_TRACING_V2\": \"true\",\n",
    "        \"LANGCHAIN_ENDPOINT\": \"https://api.smith.langchain.com\",\n",
    "        \"LANGCHAIN_PROJECT\": \"CSV-Excel-Agent\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample Data\n",
    "\n",
    "Document Used for Practice : **Titanic Dataset**\n",
    "- File Name : titanic.csv\n",
    "- Link : [Stanford CS109 Material](https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/)\n",
    "- Reference : [Titanic - Machine Learning from Disaster (Kaggle)](https://www.kaggle.com/c/titanic/data)\n",
    "\n",
    "*Please copy the downloaded file to the data folder for practice.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Siblings/Spouses Aboard</th>\n",
       "      <th>Parents/Children Aboard</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr. Owen Harris Braund</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs. John Bradley (Florence Briggs Thayer) Cum...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss. Laina Heikkinen</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs. Jacques Heath (Lily May Peel) Futrelle</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr. William Henry Allen</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                             Mr. Owen Harris Braund   \n",
       "1         1       1  Mrs. John Bradley (Florence Briggs Thayer) Cum...   \n",
       "2         1       3                              Miss. Laina Heikkinen   \n",
       "3         1       1        Mrs. Jacques Heath (Lily May Peel) Futrelle   \n",
       "4         0       3                            Mr. William Henry Allen   \n",
       "\n",
       "      Sex   Age  Siblings/Spouses Aboard  Parents/Children Aboard     Fare  \n",
       "0    male  22.0                        1                        0   7.2500  \n",
       "1  female  38.0                        1                        0  71.2833  \n",
       "2  female  26.0                        0                        0   7.9250  \n",
       "3  female  35.0                        1                        0  53.1000  \n",
       "4    male  35.0                        0                        0   8.0500  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load CSV file\n",
    "df = pd.read_csv(\"./data/titanic.csv\")\n",
    "# In case of loading an Excel file\n",
    "# df = pd.read_excel(\"./data/titanic.xlsx\", sheet_name=\"Sheet1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Analysis Agent\n",
    "\n",
    "Define an agent to analyze the data loaded from CSV or Excel files using ```create_pandas_dataframe_agent``` . \n",
    "\n",
    "This agent needs a ```PythonAstREPLTool``` to execute Python codes. Also, a custom function is defined to print the intermediate steps of the agent execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_experimental.tools import PythonAstREPLTool\n",
    "\n",
    "# Create a tool to execute Python codes.\n",
    "python_tool = PythonAstREPLTool()\n",
    "\n",
    "# Load the DataFrame on locals[\"df\"]\n",
    "python_tool.locals[\"df\"] = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "# Create an agent\n",
    "agent = create_pandas_dataframe_agent(\n",
    "    ChatOpenAI(model=\"gpt-4o\", temperature=0),\n",
    "    df,\n",
    "    verbose=False,\n",
    "    agent_type=\"tool-calling\",\n",
    "    allow_dangerous_code=True,\n",
    "    prefix=\"You are a professional data analyst. \"\n",
    "    \"You must use Pandas DataFrame(`df`) to answer user's queries. \"\n",
    "    \"\\n\\n[IMPORTANT] DO NOT create or overwrite the `df` variable in your code. \\n\\n\"\n",
    "    \"For visualization of the analyzed result, please use `plt.show()` at the end of your code. \"\n",
    "    \"I prefer seaborn for visualization, but you can use matplotlib as well.\"\n",
    "    \"\\n\\n<Visualization Preference>\\n\"\n",
    "    \"- `muted` cmap, white background, and no grid for your visualization.\"\n",
    "    \"\\nRecomment to set palette parameter for seaborn plot.\",  # Add additional instructions to the default prompt\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.agents import AgentStep\n",
    "\n",
    "\n",
    "# Define a function to stream intermediate steps of executing the agent.\n",
    "def stream_response(query):\n",
    "    response = agent.stream({\"input\": query})\n",
    "    for step in response:\n",
    "        if \"actions\" in step:\n",
    "            if step[\"actions\"][0].tool == \"python_repl_ast\":\n",
    "                tool_input = step[\"actions\"][0].tool_input\n",
    "                for k, v in tool_input.items():\n",
    "                    if k == \"query\":\n",
    "                        print(f\"---- Code Begins ----\")\n",
    "                        print(v)\n",
    "                        result = python_tool.invoke({\"query\": v})\n",
    "                        print(result)\n",
    "                        print(f\"---- Code Ends ----\")\n",
    "        elif \"steps\" in step:\n",
    "            print(f\"---- Message Begins ----\")\n",
    "            for observation in step[\"steps\"]:\n",
    "                if isinstance(observation, AgentStep):\n",
    "                    print(getattr(observation, \"observation\", None))\n",
    "            print(f\"---- Message Ends ----\")\n",
    "        elif \"output\" in step:\n",
    "            print(f\"---- Final Answer Begins ----\")\n",
    "            print(step[\"output\"])\n",
    "            print(f\"---- Final Answer Ends ----\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Code Begins ----\n",
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# Calculate the correlation matrix\n",
      "corr = df.corr()\n",
      "\n",
      "# Set up the matplotlib figure\n",
      "plt.figure(figsize=(10, 8))\n",
      "\n",
      "# Draw the heatmap with the mask and correct aspect ratio\n",
      "sns.heatmap(corr, cmap='muted', annot=True, fmt='.2f', square=True, cbar_kws={'shrink': .5}, linewidths=.5)\n",
      "\n",
      "# Set the title\n",
      "plt.title('Correlation Heatmap')\n",
      "\n",
      "# Show the plot\n",
      "plt.show()\n",
      "ValueError: could not convert string to float: 'Mr. Owen Harris Braund'\n",
      "---- Code Ends ----\n",
      "---- Message Begins ----\n",
      "ValueError: could not convert string to float: 'Mr. Owen Harris Braund'\n",
      "---- Message Ends ----\n",
      "---- Code Begins ----\n",
      "# Dropping non-numeric columns for correlation calculation\n",
      "numeric_df = df.drop(columns=['Name', 'Sex'])\n",
      "\n",
      "# Calculate the correlation matrix\n",
      "corr = numeric_df.corr()\n",
      "\n",
      "# Set up the matplotlib figure\n",
      "plt.figure(figsize=(10, 8))\n",
      "\n",
      "# Draw the heatmap with the mask and correct aspect ratio\n",
      "sns.heatmap(corr, cmap='muted', annot=True, fmt='.2f', square=True, cbar_kws={'shrink': .5}, linewidths=.5)\n",
      "\n",
      "# Set the title\n",
      "plt.title('Correlation Heatmap')\n",
      "\n",
      "# Show the plot\n",
      "plt.show()\n",
      "KeyError: \"'muted' is not a known colormap name\"\n",
      "---- Code Ends ----\n",
      "---- Message Begins ----\n",
      "KeyError: \"'muted' is not a known colormap name\"\n",
      "---- Message Ends ----\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Code Begins ----\n",
      "# Using a different colormap since 'muted' is not available\n",
      "# Calculate the correlation matrix\n",
      "corr = numeric_df.corr()\n",
      "\n",
      "# Set up the matplotlib figure\n",
      "plt.figure(figsize=(10, 8))\n",
      "\n",
      "# Draw the heatmap with the mask and correct aspect ratio\n",
      "sns.heatmap(corr, cmap='coolwarm', annot=True, fmt='.2f', square=True, cbar_kws={'shrink': .5}, linewidths=.5)\n",
      "\n",
      "# Set the title\n",
      "plt.title('Correlation Heatmap')\n",
      "\n",
      "# Show the plot\n",
      "plt.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---- Code Ends ----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Message Begins ----\n",
      "\n",
      "---- Message Ends ----\n",
      "---- Final Answer Begins ----\n",
      "I have visualized the correlations among the numeric columns in the dataset using a heatmap. The heatmap displays the correlation coefficients, with a color gradient representing the strength and direction of the correlations.\n",
      "---- Final Answer Ends ----\n"
     ]
    }
   ],
   "source": [
    "stream_response(\"Visualize correlations as a heatmap.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Code Begins ----\n",
      "len(df)\n",
      "887\n",
      "---- Code Ends ----\n",
      "---- Message Begins ----\n",
      "887\n",
      "---- Message Ends ----\n",
      "---- Final Answer Begins ----\n",
      "The DataFrame contains 887 rows.\n",
      "---- Final Answer Ends ----\n"
     ]
    }
   ],
   "source": [
    "stream_response(\"How many rows are there?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Code Begins ----\n",
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# Calculate survival rates by gender\n",
      "gender_survival_rates = df.groupby('Sex')['Survived'].mean()\n",
      "\n",
      "# Plot the survival rates\n",
      "sns.set_theme(style='white', palette='muted')\n",
      "ax = sns.barplot(x=gender_survival_rates.index, y=gender_survival_rates.values)\n",
      "ax.set_title('Survival Rates by Gender')\n",
      "ax.set_ylabel('Survival Rate')\n",
      "ax.set_xlabel('Gender')\n",
      "plt.show()\n",
      "\n",
      "---- Code Ends ----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Message Begins ----\n",
      "\n",
      "---- Message Ends ----\n",
      "---- Final Answer Begins ----\n",
      "The visualization above shows the difference in survival rates between men and women. The bar plot indicates that women had a higher survival rate compared to men.\n",
      "---- Final Answer Ends ----\n"
     ]
    }
   ],
   "source": [
    "stream_response(\"What is the difference in survival rates between men and women?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:4: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Code Begins ----\n",
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# Set the style and palette\n",
      "sns.set(style=\"white\", palette=\"muted\")\n",
      "\n",
      "# Create a barplot for survival rates by gender\n",
      "sns.barplot(x='Sex', y='Survived', data=df, ci=None)\n",
      "\n",
      "# Show the plot\n",
      "plt.show()\n",
      "\n",
      "---- Code Ends ----\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:4: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Message Begins ----\n",
      "\n",
      "---- Message Ends ----\n",
      "---- Final Answer Begins ----\n",
      "The barplot visualizing the survival rates of male and female passengers has been displayed.\n",
      "---- Final Answer Ends ----\n"
     ]
    }
   ],
   "source": [
    "stream_response(\n",
    "    \"Visualize the survival rates of male and female passengers in a barplot.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Code Begins ----\n",
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# Filter the DataFrame for children under 10 in 1st and 2nd class\n",
      "children_df = df[(df['Age'] < 10) & (df['Pclass'].isin([1, 2]))]\n",
      "\n",
      "# Create a bar plot for survival rates by sex\n",
      "sns.set_theme(style=\"white\", palette=\"muted\")\n",
      "plt.figure(figsize=(8, 6))\n",
      "sns.barplot(data=children_df, x='Sex', y='Survived', hue='Pclass')\n",
      "plt.title('Survival Rates of Male and Female Children Under 10 in 1st and 2nd Class')\n",
      "plt.ylabel('Survival Rate')\n",
      "plt.xlabel('Sex')\n",
      "plt.legend(title='Pclass')\n",
      "plt.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---- Code Ends ----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Message Begins ----\n",
      "\n",
      "---- Message Ends ----\n",
      "---- Final Answer Begins ----\n",
      "The visualization of survival rates for male and female children under 10 in 1st and 2nd class has been generated.\n",
      "---- Final Answer Ends ----\n"
     ]
    }
   ],
   "source": [
    "stream_response(\n",
    "    \"Visualize the survival rates of male and female children under 10 in 1st and 2nd class.\"\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "langchain-kr-ARohChI8-py3.11",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
